Survival of ship biofouling assemblages during and after voyages to the Canadian Arctic
Bibliographic record
Abstract
Human-mediated vectors often inadvertently translocate species assemblages to new environments. Examining the dynamics of entrained species assemblages during transport can provide insights into the introduction risk associated with these vectors. Ship biofouling is a major transport vector of nonindigenous species in coastal ecosystems globally, yet its magnitude in the Arctic is poorly understood. To determine whether biofouling organisms on ships can survive passages in Arctic waters, we examined how biofouling assemblage structure changed before, during, and after eight round-trip military voyages from temperate to Arctic ports in Canada. Species richness first decreased (~70% loss) and then recovered (~27% loss compared to the original assemblages), as ships travelled to and from the Arctic, respectively, whereas total abundance typically declined over time (~55% total loss). Biofouling community structure differed significantly before and during Arctic transits as well as between those sampled during and after voyages. Assemblage structure varied across different parts of the hull; however, temporal changes were independent of hull location, suggesting that niche areas did not provide protection for biofouling organisms against adverse conditions in the Arctic. Biofouling algae appear to be more tolerant of transport conditions during Arctic voyages than are mobile, sessile, and sedentary invertebrates. Our results suggest that biofouling assemblages on ships generally have poor survivorship during Arctic voyages. Nonetheless, some potential for transporting nonindigenous species to the Arctic via ship biofouling remains, as at least six taxa new to the Canadian Arctic, including a nonindigenous cirripede, appeared to have survived transits from temperate to Arctic ports.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".